Noise processing method, device, equipment and computer storage medium

Abstract

The present application is applicable to the field of laser radar technology, and provides a noise processing method, device, equipment and computer storage medium, the above method including: identifying noise points and normal points in point cloud data, the point cloud data contains multiple point cloud points, and the normal point is the point cloud point used to detect the target; judging whether the adjacent point cloud points within a preset range of the noise point are normal points; if the adjacent point cloud point is a normal point, then determining whether the noise point and the adjacent point cloud point are point cloud points on the same target based on the falling edge signal of the noise point and the falling edge signal of the normal point; if the noise point and the adjacent point cloud point are point cloud points on the same target, then correcting the noise point based on the adjacent point cloud point, and the corrected noise point is used to detect the target. The noise processing method provided by the present application effectively avoids the situation where all noise points are directly filtered out in the point cloud data, retains the useful information in the point cloud data processing as much as possible, and thus improves the detection results of the laser radar.

Description

Technical Field

[0001] The present application relates to the field of laser radar systems, and in particular to a noise processing method, device, equipment and computer storage medium. Background Art

[0002] LiDAR is widely used in autonomous vehicles due to its high resolution, strong anti-interference capabilities, rich information acquisition, and 24 / 7 operation. LiDAR detects targets by acquiring a large collection of point data (i.e., point cloud data) on the target surface. However, in extreme weather conditions such as rain, snow, fog, and haze, the point cloud data captured by LiDAR during target identification can contain significant noise, leading to inaccurate detection results and affecting the autonomous vehicle's decision-making process.

[0003] Currently, morphological filtering is often used to process noise in point cloud data, which directly removes noise from the point cloud data. This method may filter out a large amount of useful information at the same time, greatly reducing the detection results of LiDAR. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to provide a noise processing method, device, equipment and computer storage medium, aiming to utilize useful information in the noise to improve the detection results of the lidar.

[0005] The embodiments of the present application are implemented as follows: in the first aspect, the embodiments of the present application provide a noise processing method, the method comprising: identifying noise points and normal points in point cloud data, the point cloud data comprising multiple point cloud points, the normal points being point cloud points used to detect targets; determining whether adjacent point cloud points within a preset range of the noise point are normal points; if the adjacent point cloud points are normal points, determining whether the noise point and the adjacent point cloud points are point cloud points on the same target based on the falling edge signal of the noise point and the falling edge signal of the adjacent point cloud points; if the noise point and the adjacent point cloud points are point cloud points on the same target, correcting the noise point based on the adjacent point cloud points, and the corrected noise point is used to detect targets.

[0006] This application proposes a noise processing method that does not directly filter out noise points from point cloud data acquired by a LiDAR. Instead, after identifying a noise point, if its neighboring point cloud points are determined to be normal points and the noise point and the neighboring point cloud points are on the same target, the noise point is corrected using the neighboring point cloud points. The corrected noise point can be used to detect the target, preserving some of the useful information indicated by the corrected noise point, thereby improving the LiDAR detection results.

[0007] Optionally, the method for determining whether the noise point and the adjacent point cloud point are point cloud points on the same target includes: according to the formula Determine whether the noise point and the adjacent point cloud points are point cloud points on the same target, where T represents the preset threshold, i represents the timestamp corresponding to the falling edge signal, x1(i) represents the voltage value of the falling edge of one of the noise point and the adjacent point cloud points at time i, x2(i) represents the voltage value of the falling edge of the other point cloud point at time i, and n represents the number of timestamps in the falling edge signal.

[0008] Optionally, correcting the noise point according to the adjacent point cloud points includes: correcting the scanning distance of the noise point according to the scanning distance of at least one normal point located within a preset scanning angle of the adjacent point cloud points.

[0009] Optionally, a normal point in the point cloud data is identified by obtaining a rising edge signal of the point cloud point in the point cloud data; if the smoothness of the rising edge signal meets a preset smoothness, the point cloud point is determined to be a normal point.

[0010] Optionally, the above method also includes: if the smoothness of the rising edge signal does not meet the preset smoothness, obtaining the upper half signal of the rising edge signal of the point cloud point; if the scanning distance of the point cloud point can be calculated based on the shape of the upper half signal, then determining that the point cloud point is a normal point.

[0011] Optionally, the method of correcting the scanning distance of the noise point includes at least one of a spline interpolation method and a linear interpolation method.

[0012] Optionally, the above method further includes: if the noise point and the adjacent point cloud points are not point cloud points on the same target, deleting the noise point.

[0013] In a second aspect, an embodiment of the present application provides a noise processing device, comprising:

[0014] An identification unit, used to identify noise points and normal points in point cloud data, where the point cloud data contains multiple point cloud points, and the normal points are point cloud points used to detect targets;

[0015] A judgment unit, configured to judge whether a point in a cloud of adjacent points within a preset range of a noise point is a normal point;

[0016] a determination unit, configured to determine, if the adjacent point cloud point is a normal point, whether the noise point and the adjacent point cloud point are point cloud points on the same target based on a falling edge signal of the noise point and a falling edge signal of the adjacent point cloud point;

[0017] The correction unit is used to correct the noise point according to the adjacent point cloud points if the noise point and the adjacent point cloud points are point cloud points on the same target, and the corrected noise point is used to detect the target.

[0018] In a third aspect, an embodiment of the present application provides a terminal device, comprising: a processor and a memory, the memory being used to store a computer program, the processor being used to call and run the computer program from the memory, so that the device executes any one of the methods described in the first aspect.

[0019] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the processor executes any one of the methods described in the first aspect.

[0020] In a fifth aspect, an embodiment of the present application provides a computer program product, comprising: a computer program code, which, when executed by a computer, enables the computer to execute any one of the methods described in the first aspect.

[0021] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0023] Figure 1 1 is a comparative schematic diagram of a signal waveform of point cloud data provided in an embodiment of the present application;

[0024] Figure 2 This is a flow chart of a noise processing method provided in an embodiment of the present application;

[0025] Figure 3 is a schematic diagram of the signal waveform of a point cloud point on the same target provided by an embodiment of the present application;

[0026] Figure 4 is a structural diagram of a noise processing device provided in an embodiment of the present application;

[0027] Figure 5 A schematic diagram of the structure of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0029] Before explaining the embodiments of the present application in detail, a brief introduction to the laser radar, noise points, and normal points in the embodiments of the present application is first given.

[0030] LiDAR (LiDAR) is a sensor that uses laser beams to detect the position, three-dimensional structure, and other features of a target. Its operating process is as follows: The LiDAR transmitter emits a laser beam, which strikes the target surface and then returns to the receiver via diffuse reflection. The time interval between the laser beam's emission and the return signal's reception by the receiver, as well as the signal waveform, can be used to analyze the distance between the LiDAR and the target object. By controlling the transmitter to emit the laser beam in different directions and at different angles, a large number of return signals from the target surface can be acquired. These numerous return signals form point cloud data, with each return signal representing a point in the point cloud. This point cloud data allows the detection of the target's three-dimensional contour, position, and other information. LiDAR, due to its high resolution, strong anti-interference capabilities, rich information acquisition, and 24 / 7 operation, is widely used in autonomous vehicles.

[0031] However, when using LiDAR to detect targets in extreme weather conditions such as rain, snow, fog, haze, or frost, the laser beam may be obstructed by suspended particles such as rain, snow, and fog, causing the waveform of the echo signal received by the receiver to be deformed. Alternatively, when there is water on the road, when a car passes through the flooded road surface and splashes water, the laser beam will be hit on the ground after passing through the splash, causing the receiver to receive a signal that is a superposition of the echo signal from the splash and the return signal from the ground. In this case, the point cloud points where the signal waveform received by the receiver is deformed are noise points. In normal weather conditions (for example, there are no suspended particles and no water on the road), the point cloud points collected by the receiver are normal points.

[0032] For example, Figure 1 , which is a schematic diagram for comparing signal waveforms of a point cloud point provided by an embodiment of the present application, Figure 1 (a) is the signal waveform corresponding to the normal point in the point cloud data. Figure 1 (b) in the figure is the signal waveform corresponding to the noise point in the point cloud data. Figure 1The horizontal axis represents time, and the vertical axis represents voltage. It can be seen that compared to normal points, the rising edge signal of the noise point (i.e., the signal area in the signal waveform where the voltage changes from a low voltage value to a high voltage value over time) is significantly deformed.

[0033] Currently, to prevent useless noise from affecting detection results, morphological filtering is often used to remove noise from point cloud data. This involves filtering out normal points in the point cloud data through waveform comparison and directly removing noise. This approach can also delete a large amount of useful information, significantly reducing the reliability of LiDAR detection results.

[0034] To address the above issues, this application provides a noise processing method. After identifying a noise point, it uses normal points that belong to the same target and are adjacent to the noise point to correct the noise point, so that the corrected noise point can be used to detect the target. This avoids the loss of a large amount of useful information caused by directly deleting the noise point, thereby improving the reliability of the lidar detection results.

[0035] The technical solution of the present application is exemplarily described below with reference to specific embodiments.

[0036] In one possible implementation, Figure 2 The figure shows a flow chart of a noise processing method provided by this application. Figure 2 , the method includes the following steps.

[0037] S101, identifying noise points and normal points in point cloud data, where the point cloud data includes multiple point cloud points, and the normal points are point cloud points used to detect targets.

[0038] In an embodiment of the present application, signal waveform similarity comparison can be used to determine whether each point in the point cloud data is a noise point or a normal point. For example, a standard signal waveform is preset. The signal waveform of each point in the point cloud data is matched with the standard signal waveform for similarity. If the similarity is greater than a preset value, the point cloud point is determined to be a normal point. If the similarity is less than the preset value, the point cloud point is determined to be a noise point.

[0039] In one example, the smoothness of the rising edge signal in the signal waveform of a point cloud point determines whether the scanning distance of the point cloud point can be calculated. Therefore, the present application provides a point cloud point recognition method that can identify whether the point cloud point is a noise point or a normal point based on the rising edge signal of each point cloud point.

[0040] For example, consider a point in a point cloud. After acquiring the rising edge signal for that point cloud point, the smoothness of the rising edge signal is determined to meet a preset smoothness level. The preset smoothness level is defined as a level sufficient to determine the scanning distance. If the rising edge signal smoothness meets the preset smoothness level, the point cloud point is determined to be a normal point.

[0041] Correspondingly, if the smoothness of the rising edge signal does not meet the preset smoothness, the point cloud point is determined to be a noise point.

[0042] Optionally, if the smoothness of the rising edge signal of the signal waveform does not meet a preset smoothness, the upper half of the rising edge signal of the point cloud point can be obtained. Based on the shape of the upper half signal, it is determined whether the rising edge signal can be used to calculate the scanning distance of the point cloud point. For example, by determining whether the shape of the upper half signal meets a preset smoothness. If it does, it can be considered that the scanning distance can be calculated.

[0043] In this example, if the scanning distance of the point cloud point can be obtained based on the shape of the signal of the upper half of the point cloud point, the point cloud point is determined to be a normal point. Otherwise, the point cloud point is identified as a noise point.

[0044] The rising edge signal can be divided into two parts according to time, and the later part is the upper half, that is, the part of the signal with a larger corresponding voltage value is the upper half signal.

[0045] S102: Determine whether a point in the adjacent point cloud within a preset range of the noise point is a normal point.

[0046] The preset range may be a range of adjacent scanning angles of the noise point. For example, assuming the initial scanning angle of the LiDAR is 0° and the scanning angle resolution of the LiDAR device is 0.1°, the scanning angles of the point cloud data are 0°, 0.1°, 0.2°, 0.3°, 0.4°, etc. In this case, if the noise point is a point cloud point scanned at 0.3°, the preset range may be within the range of scanning angles of 0.1°, 0.2°, 0.4°, and 0.5°.

[0047] S103: If the adjacent point cloud point is a normal point, determine whether the noise point and the adjacent point cloud point are point cloud points on the same target based on the falling edge signal of the noise point and the falling edge signal of the adjacent point cloud point.

[0048] Generally speaking, each point cloud point in the point cloud data of the same target acquired by the lidar has the same distance and the same reflectivity. Therefore, within the allowable error range, the shape of the signal waveform formed by the point cloud points on the same target is the same. However, in extreme weather such as rain, snow, fog, and haze, the rising edge of the signal waveform formed by the point cloud points on the same target will be affected, while the falling edge will be less affected. Therefore, the degree of overlap of the falling edge signal between the noise point in the point cloud point and the adjacent point cloud points can be used to determine whether they are points on the same target.

[0049] For example, the signal waveforms of multiple point cloud points on the same target can be as follows Figure 3 Regardless of whether the rising edge signals of each point cloud point have waveform changes, the waveforms of the falling edge signals basically coincide.

[0050] For example, the formula Determine whether the noise point and the adjacent point cloud points are point cloud points on the same target, where T represents the preset threshold, i represents the timestamp corresponding to the falling edge signal, x1(i) represents the voltage value of the falling edge of one of the noise point and the adjacent point cloud points at time i, x2(i) represents the voltage value of the falling edge of the other point cloud point at time i, and n represents the number of timestamps in the falling edge signal.

[0051] That is, if the voltage values corresponding to each point on the falling edge of the noise point and the adjacent point cloud point satisfy the above formula, then the noise point and the adjacent point cloud point are determined to be points on the same target.

[0052] The above method provides fault tolerance for determining whether the noise point and the adjacent point cloud points are point cloud points collected from the same target, increases the probability that the noise point and the adjacent point cloud points are point cloud points collected from the same target, and avoids the situation where the noise point and the adjacent point cloud points are determined to be point cloud points collected from the same target only when the falling edges of the signal waveforms of the noise point and the adjacent point cloud points completely overlap.

[0053] S104: If the noise point and the adjacent point cloud points are point cloud points on the same target, the noise point is corrected according to the adjacent point cloud points, and the corrected noise point is used to detect the target.

[0054] In one example, the scanning distance of a noise point may be corrected based on the scanning distance of at least one normal point located within a preset scanning angle of an adjacent point cloud point.

[0055] For example, assuming that the initial scanning angle of the laser radar is 0° and the scanning angle resolution of the laser radar device is 0.1°, the scanning angles of the point cloud data are 0°, 0.1°, 0.2°, 0.3°, 0.4°, etc. In this case, if the scanning angle of the adjacent point cloud point is 0.2°, then the normal point within the preset scanning angle of the adjacent point cloud point can be a normal point in the point cloud points with scanning angles of 0.1° and 0.3°, or a normal point in the point cloud points with scanning angles of 0°, 0.1°, 0.3° and 0.4°. Therefore, according to the accuracy of the point cloud data obtained by different laser radars, the preset scanning angle can be flexibly adjusted to find normal points around the adjacent point cloud points.

[0056] In this example, if only one normal point that meets the requirements exists among the neighboring points of a noise point in the point cloud data, the noise point can be corrected by combining at least one normal point within a preset scanning angle around the normal point to improve the effectiveness of the corrected noise point and enhance the accuracy of LiDAR detection. Of course, if only multiple normal points exist among the neighboring points of the noise point, the noise point can be corrected by combining at least one normal point within a preset scanning angle around each of the multiple normal points.

[0057] Optionally, if there are at least two normal points in the neighboring point cloud points within a preset range of the noise point, the noise point may be directly corrected based on the at least two normal points.

[0058] Exemplarily, the scanning distance of the noise point can be corrected using at least one of a spline interpolation method and a linear interpolation method. Using the spline interpolation method or the linear interpolation method to correct the position parameters of the noise point reduces computational complexity and improves noise point processing efficiency.

[0059] In a possible implementation, after S103 above, if the neighboring point cloud points within the preset range of the noise point are normal points, but the noise point and each neighboring point cloud point that is a normal point are not point cloud points on the same target, the noise point is deleted.

[0060] In some embodiments, if there are no normal points in the neighboring point cloud points within a preset range of the noise point, the noise point can be directly deleted.

[0061] The present application provides a noise processing method which does not directly filter out noise in point cloud data acquired by a laser radar through filtering or other methods. Instead, the method first identifies noise and normal points from the point cloud data, and then determines whether there are normal points in the point cloud points adjacent to the noise point. If there are normal points, the method further determines whether the noise point and the adjacent point cloud points are points on the same target. When the noise point and the adjacent point cloud points are points on the same target, the noise point is corrected using the adjacent point cloud points. The noise point corrected using the adjacent point cloud points can be used to detect the target, which effectively avoids the situation where all noise points in the point cloud data are directly filtered out, and retains as much useful information in the point cloud data processing as possible, thereby improving the detection results of the laser radar.

[0062] Based on the same inventive concept, the embodiment of the present application also provides a noise processing device. Figure 4 As shown, the device 200 includes: an identification unit 201, a judgment unit 202, a determination unit 203 and a correction unit 204.

[0063] The identification unit 201 is used to identify noise points and normal points in the point cloud data. The point cloud data includes multiple point cloud points. The normal points are point cloud points used to detect targets.

[0064] A judging unit 202 is configured to judge whether a point in a cloud of adjacent points within a preset range of a noise point is a normal point;

[0065] a determination unit 203 for determining, if the adjacent point cloud point is a normal point, whether the noise point and the adjacent point cloud point are point cloud points on the same target based on a falling edge signal of the noise point and a falling edge signal of the adjacent point cloud point;

[0066] The correction unit 204 is configured to correct the noise point based on the adjacent point cloud points if the noise point and the adjacent point cloud points are point cloud points on the same target, and use the corrected noise point for detecting the target.

[0067] Optionally, the determining unit 203 is further configured to: Determine whether the noise point and the adjacent point cloud points are point cloud points on the same target, where T represents the preset threshold, i represents the timestamp corresponding to the falling edge signal, x1(i) represents the voltage value of the falling edge of one of the noise point and the adjacent point cloud points at time i, x2(i) represents the voltage value of the falling edge of the other point cloud point at time i, and n represents the number of timestamps in the falling edge signal.

[0068] Optionally, the correction unit 204 is further configured to correct the scanning distance of the noise point according to the scanning distance of at least one normal point located within a preset scanning angle of the adjacent point cloud point.

[0069] Optionally, the identification unit 201 is further configured to obtain a rising edge signal of a point cloud point in the point cloud data; if the smoothness of the rising edge signal meets a preset smoothness, the point cloud point is determined to be a normal point.

[0070] Optionally, the above method also includes: if the smoothness of the rising edge signal does not meet the preset smoothness, obtaining the upper half signal of the rising edge signal of the point cloud point; according to the shape of the upper half signal, determining whether the rising edge signal can calculate the scanning distance of the point cloud point; if the scanning distance of the point cloud point can be obtained, determining that the point cloud point is a normal point.

[0071] Optionally, the correction unit 204 is further configured to select at least one of a spline interpolation method and a linear interpolation method to correct the scanning distance of the noise point.

[0072] Optionally, the above-mentioned device further includes: a deleting unit, configured to delete the noise point if the noise point and the normal point are not point cloud points on the same target.

[0073] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0074] Based on the same inventive concept, the embodiment of the present application also provides a terminal device. Figure 5 As shown, the terminal device 300 of this embodiment includes: a processor 301, a memory 302, and a computer program 303 stored in the memory 302 and executable on the processor 301. The computer program 303 can be executed by the processor 301 to generate instructions, and the processor 301 can implement the steps of the above-mentioned various noise processing method embodiments according to the instructions. Alternatively, when the processor 301 executes the computer program 303, the functions of the modules / units in the above-mentioned various device embodiments are implemented, such as Figure 4 The functions of the identification unit 201, the judgment unit 202, the determination unit 203 and the correction unit 204 are shown.

[0075] Exemplarily, computer program 303 may be divided into one or more modules / units, one or more of which are stored in memory 302 and executed by processor 301 to implement the present application. One or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of computer program 303 in terminal device 300.

[0076] Those skilled in the art will understand that Figure 5 It is merely an example of the terminal device 300 and does not constitute a limitation of the terminal device 300. The terminal device 300 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device 300 may also include input and output devices, network access devices, buses, etc.

[0077] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0078] The memory 302 can be an internal storage unit of the terminal device 300, such as a hard disk or memory of the terminal device 300. The memory 302 can also be an external storage device of the terminal device 300, such as a plug-in hard disk equipped on the terminal device 300, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Furthermore, the memory 302 can also include both the internal storage unit of the terminal device 300 and an external storage device. The memory 302 is used to store computer programs and other programs and data required by the terminal device 300. The memory 302 can also be used to temporarily store data that has been output or is about to be output.

[0079] The terminal device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effects are similar, which will not be repeated here.

[0080] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method described in the above method embodiment is implemented.

[0081] An embodiment of the present application further provides a computer program product, which, when executed on a terminal device, enables the terminal device to implement the method described in the above method embodiment.

[0082] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include at least: any entity or device capable of carrying computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0083] References to "one embodiment" or "some embodiments" in this application mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0084] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, features specified as "first" or "second" may explicitly or implicitly include at least one of such features.

[0085] In addition, in this application, unless otherwise clearly specified and limited, the terms "connection", "connected", etc. should be understood in a broad sense. For example, it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal connection between two elements or the interaction relationship between two elements. Unless otherwise clearly specified, ordinary technicians in this field can understand the specific meanings of the above terms in this application according to specific circumstances.

[0086] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A noise processing method, characterized in that: The method comprises: Identifying noise points and normal points in point cloud data, wherein the point cloud data includes a plurality of point cloud points, and the normal points are point cloud points used to detect targets; Determine whether adjacent point cloud points within a preset range of the noise point are normal points; If the adjacent point cloud point is a normal point, determining whether the noise point and the adjacent point cloud point are point cloud points on the same target according to the falling edge signal of the noise point and the falling edge signal of the adjacent point cloud point; If the noise point and the adjacent point cloud points are point cloud points on the same target, the noise point is corrected according to the adjacent point cloud points, and the corrected noise point is used to detect the target.

2. The method according to claim 1, characterized in that The method for determining whether the noise point and the adjacent point cloud point are point cloud points on the same target includes: According to the formula Determine whether the noise point and the adjacent point cloud point are point cloud points on the same target, where T represents a preset threshold, i represents the timestamp corresponding to the falling edge signal, x1(i) represents the voltage value of the falling edge of one of the noise point and the adjacent point cloud point at time i, x2(i) represents the voltage value of the falling edge of the other point cloud point at time i, and n represents the number of timestamps in the falling edge signal.

3. The method according to claim 1, characterized in that The correcting the noise point according to the neighboring point cloud points includes: The scanning distance of the noise point is corrected according to the scanning distance of at least one normal point located within a preset scanning angle of the adjacent point cloud point.

4. The method according to claim 1, wherein The identification method of normal points in the point cloud data is: Get the rising edge signal of the point cloud point in the point cloud data; If the smoothness of the rising edge signal meets the preset smoothness, the point cloud point is determined to be a normal point.

5. The method according to claim 4, characterized in that The method further comprises: If the smoothness of the rising edge signal does not meet the preset smoothness, obtaining the upper half signal of the rising edge signal of the point cloud point; If the scanning distance of the point cloud point can be calculated based on the shape of the upper half signal, then the point cloud point is determined to be a normal point.

6. The method according to claim 3, characterized in that The method of correcting the scanning distance of the noise point includes at least one of a spline interpolation method and a linear interpolation method.

7. The method according to claim 1, characterized in that The method further comprises: If the noise point and the adjacent point cloud point are not point cloud points on the same target, the noise point is deleted.

8. A noise processing device, characterized in that: The device comprises: an identification unit, configured to identify noise points and normal points in point cloud data, wherein the point cloud data includes a plurality of point cloud points, and the normal points are point cloud points used to detect targets; A judging unit, configured to judge whether a point in the adjacent point cloud within a preset range of the noise point is a normal point; a determining unit, configured to determine, if the adjacent point cloud point is a normal point, whether the noise point and the adjacent point cloud point are point cloud points on the same target based on a falling edge signal of the noise point and a falling edge signal of the adjacent point cloud point; A correction unit is used to correct the noise point according to the adjacent point cloud points if the noise point and the adjacent point cloud points are point cloud points on the same target, and the corrected noise point is used to detect the target.

9. A terminal device, characterized in that: The device comprises: a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 7.

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